Literature DB >> 32220870

Limits of trust in medical AI.

Joshua James Hatherley1.   

Abstract

Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI's progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  ethics; information technology; quality of health care

Year:  2020        PMID: 32220870     DOI: 10.1136/medethics-2019-105935

Source DB:  PubMed          Journal:  J Med Ethics        ISSN: 0306-6800            Impact factor:   2.903


  7 in total

1.  Trust does not need to be human: it is possible to trust medical AI.

Authors:  Andrea Ferrario; Michele Loi; Eleonora Viganò
Journal:  J Med Ethics       Date:  2020-11-25       Impact factor: 2.903

Review 2.  Applications of interpretability in deep learning models for ophthalmology.

Authors:  Adam M Hanif; Sara Beqiri; Pearse A Keane; J Peter Campbell
Journal:  Curr Opin Ophthalmol       Date:  2021-09-01       Impact factor: 4.299

3.  Behavioral Ethics Ecologies of Human-Artificial Intelligence Systems.

Authors:  Stephen Fox
Journal:  Behav Sci (Basel)       Date:  2022-04-11

4.  Explainable Artificial Intelligence for Predicting Hospital-Acquired Pressure Injuries in COVID-19-Positive Critical Care Patients.

Authors:  Jenny Alderden; Susan M Kennerly; Andrew Wilson; Jonathan Dimas; Casey McFarland; David Y Yap; Lucy Zhao; Tracey L Yap
Journal:  Comput Inform Nurs       Date:  2022-10-01       Impact factor: 2.146

5.  Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework.

Authors:  Jinxin Yang; Biao Luo; Chen Zhao; Hongliang Zhang
Journal:  Digit Health       Date:  2022-10-05

Review 6.  Expectations for Artificial Intelligence (AI) in Psychiatry.

Authors:  Scott Monteith; Tasha Glenn; John Geddes; Peter C Whybrow; Eric Achtyes; Michael Bauer
Journal:  Curr Psychiatry Rep       Date:  2022-10-10       Impact factor: 8.081

7.  Design publicity of black box algorithms: a support to the epistemic and ethical justifications of medical AI systems.

Authors:  Andrea Ferrario
Journal:  J Med Ethics       Date:  2021-05-12       Impact factor: 5.926

  7 in total

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